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Joint replacement is indicated for patients suffering from disability due to:

• · noninflammatory degenerative joint disease including osteoarthritis and avascular necrosis of the natural femoral head;
• rheumatoid arthritis:
• · correction of functional deformity;
• · femoral fracture.

This device may also be indicated in the salvage of previously failed surgical attempts.

This device is indicated for cementless use.

The DJO Acetabular System allows for the total replacement of the acetabulum. The system consists of porous coated titanium acetabular cups, bone screws for use with the cups, and acetabular liners manufactured from highly crossed linked polyethylene with Vitamin E. It is designed for compatibility with currently cleared DJO stems and femoral heads.

This looks like a 510(k) summary for a medical device that does not involve AI. Therefore, most of the questions about acceptance criteria, study design, ground truth, and expert involvement are not applicable in the context of AI/ML devices.

Here's a breakdown based on the provided document:

1. A table of acceptance criteria and the reported device performance

The provided document describes a traditional medical device (hip implant) and does not specify quantitative acceptance criteria in the way you would expect for an AI/ML device (e.g., sensitivity, specificity thresholds). Instead, the acceptance criteria are implicitly that the device performs equivalently to the predicates in non-clinical mechanical testing.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This device clearance did not involve clinical testing as stated: "Clinical Testing: Clinical testing was not required". Therefore, there is no "test set" of patient data in the context of an AI/ML study. The "testing" referred to is mechanical testing of the physical device components. The document does not specify sample sizes for these mechanical tests, nor is data provenance (country, retrospective/prospective) relevant here.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable, as no clinical testing or AI/ML ground truth was established by experts.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable, as no clinical testing or AI/ML ground truth was established.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This is a physical hip implant device, not an AI/ML diagnostic or assistive tool.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. There is no algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not applicable. For mechanical testing, the "ground truth" is typically defined by engineering specifications, material properties, and comparison to predicate device performance under controlled laboratory conditions, rather than clinical outcomes or expert consensus on a diagnosis.

8. The sample size for the training set

Not applicable. There is no AI/ML model to train.

9. How the ground truth for the training set was established

Not applicable. There is no AI/ML model to train.

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